ToFFi – Toolbox for frequency-based fingerprinting of brain signals
Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versat...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2023
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| Subjects: | |
| Online Access: | View Fulltext in Publisher View in Scopus |
| LEADER | 02112nam a2200385Ia 4500 | ||
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| 001 | 10.1016-j.neucom.2023.126236 | ||
| 008 | 230529s2023 CNT 000 0 und d | ||
| 020 | |a 09252312 (ISSN) | ||
| 245 | 1 | 0 | |a ToFFi – Toolbox for frequency-based fingerprinting of brain signals |
| 260 | 0 | |b Elsevier B.V. |c 2023 | |
| 856 | |z View Fulltext in Publisher |u https://doi.org/10.1016/j.neucom.2023.126236 | ||
| 856 | |z View in Scopus |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159050810&doi=10.1016%2fj.neucom.2023.126236&partnerID=40&md5=3c643b183dec99f4f058ae6363c62a9c | ||
| 520 | 3 | |a Spectral fingerprints (SFs) are unique power spectra signatures of human brain regions of interest (ROIs, Keitel & Gross, 2016). SFs allow for accurate ROI identification and can serve as biomarkers of differences exhibited by non-neurotypical groups. At present, there are no open-source, versatile tools to calculate spectral fingerprints. We have filled this gap by creating a modular, highly-configurable MATLAB Toolbox for Frequency-based Fingerprinting (ToFFi). It can transform magnetoencephalographic and electroencephalographic signals into unique spectral representations using ROIs provided by anatomical (AAL, Desikan-Killiany), functional (Schaefer), or other custom volumetric brain parcellations. Toolbox design supports reproducibility and parallel computations. © 2023 | |
| 650 | 0 | 4 | |a Biomarkers |
| 650 | 0 | 4 | |a Brain |
| 650 | 0 | 4 | |a Brain fingerprinting |
| 650 | 0 | 4 | |a Brain regions |
| 650 | 0 | 4 | |a Brain signals |
| 650 | 0 | 4 | |a Computational neuroscience |
| 650 | 0 | 4 | |a Electroencephalography |
| 650 | 0 | 4 | |a Human brain |
| 650 | 0 | 4 | |a Power-spectra |
| 650 | 0 | 4 | |a Region-of-interest |
| 650 | 0 | 4 | |a Regions of interest |
| 650 | 0 | 4 | |a Source localization |
| 650 | 0 | 4 | |a Spectral fingerprints |
| 700 | 1 | 0 | |a Dreszer, J. |e author |
| 700 | 1 | 0 | |a Duch, W. |e author |
| 700 | 1 | 0 | |a Jurewicz, K. |e author |
| 700 | 1 | 0 | |a Keitel, A. |e author |
| 700 | 1 | 0 | |a Komorowski, M.K. |e author |
| 700 | 1 | 0 | |a Piotrowski, T. |e author |
| 700 | 1 | 0 | |a Rykaczewski, K. |e author |
| 700 | 1 | 0 | |a Wojciechowski, J. |e author |
| 773 | |t Neurocomputing | ||